{
 "metadata": {
  "name": "Patch-Based Feature Extraction for Image Classification"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import pylab as pl\n",
      "from sklearn.datasets import fetch_olivetti_faces\n",
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn.utils import shuffle\n",
      "from scipy.stats.mstats import mquantiles\n",
      "pl.gray()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d45e650>"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "olivetti = fetch_olivetti_faces()\n",
      "faces = olivetti.images\n",
      "faces_train, faces_test, target_train, target_test = train_test_split(\n",
      "    olivetti.images, olivetti.target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.imshow(faces_train[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.image.AxesImage at 0x10ee1fdd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PW9Azi2ED0igPcVFWRRmSRmgX5TFubECOLrNhl6K8m6O7PvB31TdHedyToc4P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\nfkM25AUmG6DfkA15gckG6DdkQ15g8v8BcdsKy+0t7QwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d42af10>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.image import PatchExtractor"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pe = PatchExtractor((8, 8), random_state=42)\n",
      "patches = pe.transform(faces_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patches.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "(974700, 8, 8)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patches = patches.reshape(-1, 64)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_patches(patches, n_rows=8, n_cols=12):\n",
      "    pl.figure(figsize=(1.2 * n_cols, 1.2 * n_rows))\n",
      "    for i in range(n_rows):\n",
      "        for j in range(n_cols):\n",
      "            idx = i * n_cols + j\n",
      "            if idx < len(patches):\n",
      "                pl.subplot(n_rows, n_cols, i * n_cols + j)\n",
      "                pl.imshow(patches[idx].reshape(8, 8), interpolation='nearest')\n",
      "                pl.axis('off')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(patches)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u8/jx42bt29/+tty2YO/evWZN3QOnT5+W46rrfPLkSbP23nvvmbW3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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10de26950>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_patches = 100000\n",
      "\n",
      "patches = shuffle(patches)[:n_patches]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(patches)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pp+S2U1TvvDp+qK9ercOjMtVlr/rizcLfURKPPfaYm6l1j9R6I2Zm3d3dbvbk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\nkiRJkor0f72AEk4R71RvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11046ad90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patches_centered = patches - patches.mean(axis=1).reshape(n_patches, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import normalize\n",
      "\n",
      "patches_normalized = normalize(patches_centered)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(patches_normalized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pp+S2U1TvvDp+qK9ercOjMtVlr/rizcLfURKPPfaYm6l1j9R6I2Zm3d3dbvbk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\nkiRJkor0f72AEk4R71RvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ee0c190>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cluster import MiniBatchKMeans"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km_10 = MiniBatchKMeans(n_clusters=10, batch_size=1000, verbose=0, n_init=3, random_state=42)\n",
      "%time km_10.fit(patches_normalized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 624 ms, sys: 77.2 ms, total: 702 ms\n",
        "Wall time: 673 ms\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
        "        init_size=None, max_iter=100, max_no_improvement=10, n_clusters=10,\n",
        "        n_init=3, random_state=42, reassignment_ratio=0.01, tol=0.0,\n",
        "        verbose=0)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centroids_10 = normalize(km_10.cluster_centers_)\n",
      "activations_10 = np.dot(patches_normalized, centroids_10.T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_activations(activations, n_rows=8, n_cols=12, bins=10):\n",
      "    pl.figure(figsize=(1.2 * n_cols, 1.2 * n_rows))\n",
      "    for i in range(n_rows):\n",
      "        for j in range(n_cols):\n",
      "            idx = i * n_cols + j\n",
      "            if idx < len(patches):\n",
      "                pl.subplot(n_rows, n_cols, i * n_cols + j)\n",
      "                pl.title(\"{:0.2f}\".format(np.median(activations[:, idx])))\n",
      "                pl.hist(activations[:, idx], bins=bins)\n",
      "                pl.axis('off')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(centroids_10, n_rows=1, n_cols=10)\n",
      "plot_activations(activations_10, n_rows=1, n_cols=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAArUAAABTCAYAAACBIv/3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0zGElCQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ee065d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAABrCAYAAAC47H5HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ee06210>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km_100 = MiniBatchKMeans(n_clusters=100, batch_size=1000, verbose=0, n_init=3, random_state=42)\n",
      "%time km_100.fit(patches_normalized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 2.38 s, sys: 105 ms, total: 2.49 s\n",
        "Wall time: 2.31 s\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
        "        init_size=None, max_iter=100, max_no_improvement=10,\n",
        "        n_clusters=100, n_init=3, random_state=42, reassignment_ratio=0.01,\n",
        "        tol=0.0, verbose=0)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centroids_100 = normalize(km_100.cluster_centers_)\n",
      "activations_100 = np.dot(patches_normalized, centroids_100.T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(centroids_100)\n",
      "plot_activations(activations_100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aVoGqx+m6SYnHWqqlzt2/Vd1///1hRtX59H5BWW4MoMp7Gk9zY21V\ntCQDjW00tlDF+8aNG/H70PVKz3h6j6uKxid6ludqoCmn+5zuz1y1NB3HDRs23PLf/cuOJEmSpCI5\n2ZEkSZJUJCc7kiRJkorkZEeSJElSkZzsSJIkSSqSkx1JkiRJRWrc70d1eVQBnKutpRpNqnilKsRc\n9TRVM1Y1Pj4eZlTDlzsedashabsTExO4z/3792Nexde+9rUwo2sndzzoXNJn6ZrMVR1T/ju/8zv4\n2Sm7du0KM/reVJmdEv8uuq6pGjR3v+S+UxV166tzn6NzRb+ZKi1zdai541XFT37ykzC78847w6yn\npwe3SxXBNH5QLTVtMyU+llVR1S/Vw+ZqoAcHB8OM6qWplnh4eBj3ef78ecyr6O7uDjO6z6lemu6H\nXJ06VddTVTod/5mIamZT4ucb1QfTWJqrD6fKXHo3WLZsGW63qre//e1hRtcAZfRORec/pfpLRLQK\nXb/0jkmV6/S5Ju8QdDxaUcV94MCBMKNnOV23KXEtNo15NH5TlhLfv0888cQt/92/7EiSJEkqkpMd\nSZIkSUVysiNJkiSpSE52JEmSJBXJyY4kSZKkIjnZkSRJklSkxn12VEtH1cK52lr6LFW8UkVfrkaT\nKjiroupJqsu7evVq433fyrVr18IsVyl48uTJxvv/+te/Hmb03XLHg64BunboGqC6yZS4grOqzs7O\nMKNaytzxoN+cq8OM0LWcUmuqlqmOvcn9Qr+ZqpZpXKp7HGfi9OnTYbZ3794wu/vuu3G7HR0dYUaV\nxfSbc/dLbryt4syZM2FGlae56mmqtKbq6aGhoTDL1RLT962qyfgVoWu+Sb08VZPT8Z+J22+/Pcyo\n+nx0dDTMaGzJjYl1q/Rb9fzfsmVLmLW3t4cZjYlNqslp/KBjRe8GM1G3/r7uu2vufqHrh/ZJWVU/\n/OEPw4zuB6rbT6l+jT9V9efGUnpveOqpp2757/5lR5IkSVKRnOxIkiRJKpKTHUmSJElFcrIjSZIk\nqUhOdiRJkiQVycmOJEmSpCLNulm3K1GSJEmS/hfzLzuSJEmSiuRkR5IkSVKRnOxIkiRJKpKTHUmS\nJElFcrIjSZIkqUhOdiRJkiQVycmOJEmSpCI52ZEkSZJUJCc7kiRJkorkZEeSJElSkZzsSJIkSSrS\nnKYbePHFF8Osu7s7zJYsWYLbvXnzZpiNjY2F2cGDB8PsF7/4Be7zlVdeCbOvfOUr+Nkp3/nOd8Js\n1qxZYXbjxg3cLh2P3GfrbDOXv+c976m1z+muXr0aZv39/fjZ48ePh9nhw4fDrLe3N8yGhoZwn9ev\nXw+zz33uc/jZKX/0R38UZn19fWE2MDCA27148WKY0fVx223x/++YN28e7nPu3Llhtm/fPvzslP37\n99faPn3vlFKaPXt2rc82uUcp37hxI362ivPnz4fZ0aNH8bM0Tu/ZsyfM6DzmrslLly6F2ZkzZ/Cz\nU55++ukwGx4eDrPcd6P83LlzYTY6OhpmFy5cwH3S8fj2t7+Nn52yePHiMPvABz4QZn/6p38aZtu3\nbw+z3DV/4MCBMPvWt74VZv/0T/8UZmfPnsV9TveFL3whzJYuXRpm9Lvoujp27Bh+H7pf6P3ixIkT\nYZZ7TldFzy86HnTdTk5O4j4pp+3SO94DDzyA+5zuySefDLPLly+HGX1v+hw9d1Lic3DlypUwo/Oz\nd+9e3GcV9JvoPS0lPlbj4+NhRuef3mdSSmliYiLM3vrWt97y3/3LjiRJkqQiOdmRJEmSVCQnO5Ik\nSZKK5GRHkiRJUpGc7EiSJEkqUuM2NmqXIAsXLsS8bisStbwtW7YM90lNN1V997vfDTP63tTEkRI3\nSFEDCDVa5ZpDqI2rahvboUOHwowaQE6dOoXbpfapI0eO1PrcyMgI7jN3zVaxaNGiMJs/f36Y5e4z\nauqiZhtq+sk1ni1YsADzKlauXBlmTdrYaPygjORakeh6roraa+gc59rHTp48GWbUbEitiHRdpZRv\n8alixYoVtT6X2ze1+VCrGl0DuetjzpzGj1tsKaLxnMad9vb2MMv9Jmo8o33WbRB9PXpG0bVDv3nT\npk1htm3bNvw+O3bsCDNqEaMmzpm4du1amNE9QWMX3ee5ZyZ9lu4zGutm0sZW9xlCzza6J+h+SInf\nT+kdsBWNfNQySc1olKXEz6y67ZX0XXOftY1NkiRJ0v8pTnYkSZIkFcnJjiRJkqQiOdmRJEmSVCQn\nO5IkSZKK5GRHkiRJUpGc7EiSJEkqUuPif+r9n5ycDLPcuiHUc07d6dS7n+tAb8W6If/5n/8ZZtRX\nnlsXgjrhaf0XWusgt64QrUVQ1Te/+c0wo47+4eFh3O7Zs2fDjDraqcs+tw5GK9bJuPPOO8Oss7Mz\nzLq6unC7tH7Q6dOnw4w68nNrP+XWaaqira0tzOhezq2VQ+t40GdpPYPcPVp3zbHphoaGwozW0smt\n01H3+qB1EnLXRyvWUqF7gp4DueuD1iOhjM5xbu2n3HoVTdH4RN+b7uPc+h60NljdbCboXNV9ZtJz\ncc2aNfh9Nm7cGGb0vMutK1IVXWN119KhZ/Hg4CB+nzNnzoQZjXW0zw9+8IO4z6r7oGuH7iV6j8w9\nE+m6o/etVjxb6FzRuzq9I6TEayLRPukdjrJc/ud//ue3/Hf/siNJkiSpSE52JEmSJBXJyY4kSZKk\nIjnZkSRJklQkJzuSJEmSiuRkR5IkSVKRGnfpUp0i1R7naqCp8pLqrqmiL1dHmsuroKq9CxcuhFmu\n4pMqDakqc8mSJWGWq0KlasaqvvjFL4ZZk1pburZWrFgRZqtXrw4zqg1NKaXly5djXsWjjz4aZlQN\nmqvi7u/vDzOqFqZqzlzlZCuqdOl7U/V0kypOqkSmemk6Pynx8di9e3f+iyWu1Dx58mSYHTp0CLfb\n29sbZnQNUCVprmqZqqGrWrlyZZjVXZ4gl1MtMlXH0nFMqTX1wjTW05jYpFKdUEUvHSuqnJ8JeobQ\n85aei5TRMU4ppWXLloVZd3d3mNE7zUzQuaTxnMadU6dOhdmRI0fw+9C4Q+9KraripuprqkwmdH3k\nruuOjo4wy11bTdE5puuvybsHPbPouqJ3ltw+I/5lR5IkSVKRnOxIkiRJKpKTHUmSJElFcrIjSZIk\nqUhOdiRJkiQVycmOJEmSpCI1rp6muk2q6MvV/lH1MFUoUt3k2NgY7pNqVqt64IEHwoyqnHO111TD\nS7XUVA+bqzqkateq9u/fH2ZUAUwVniml1NPTE2bbtm0Ls9tvvz3Mtm/fjvtsRTUkfTeSq+Kme4Lq\n4etmuX1W9dWvfjXM6H7J1aI3+WwkV3dN93DV6mmq1Dx69GiY5SpgqcqTziNV2f46xg+qNqYa5tz9\nQueSMhqzaBxOKV/VXQVV5+eWc4hQLTUd/5T4uUTnvxU1/imlNDAwEGZ0fdStHqcsJa7bpqwV90pK\nXH9P4zmNOwcPHgyzvXv34vc5fPhwmNG7Y5OlBaajum8ar+tWcZ84cQK/T933OPpcVTQm0rtv7r2Z\nziM9d44dOxZmueNo9bQkSZIk/X+c7EiSJEkqkpMdSZIkSUVysiNJkiSpSE52JEmSJBXJyY4kSZKk\nIjWunqbqR6ppzFVaUk0j1eRRJd3g4CDus06d3et96EMfCjOqHqX6z5S4lpA+S1muCjX3naqgyl26\nPlatWoXbXbduXZht2bIlzDZs2BBm69evx33m6sGroOs6V11L2tvbw6yjoyPMqKp0YmIC90k171V9\n7nOfCzOqp899N6rZpONM5ydXO0vVv5/5zGfws1PqVnVSHWpKfCwvX76c/V63QvW9KfGxrKq3tzfM\nqAY6t4wA5XQ8aEzMjQ+tqJ6myn0aA+iapyr23DOg7tIGbW1tuN2qzpw5E2Z0v9LxoOs6d83TNUBZ\nbrtVUb00vatRdf2+ffvC7KWXXsLv09fXh3kkt/REVe9617vCjMZEqj4+fvx4mNExTiml0dHRMKs7\nJlVFS8HQfZwbS6k2nd7zaZ+5d6Hcs/iW+5vxJyRJkiTpN4CTHUmSJElFcrIjSZIkqUhOdiRJkiQV\nycmOJEmSpCI52ZEkSZJUJCc7kiRJkorUeJ2dQ4cOhRmt4ZHr7qZOcFpDg3r3Dx8+jPvM5VW87W1v\nCzPqHL9+/XrtfdJnaS2K3D6vXr1a+ztN+ehHPxpmtA4DZSnxmhK0vsfy5cvDLLe2TyvW2aHrnq6P\nXO/8G7GGQ+73tmJtiIsXL4ZZ3fVOUuIefsro2qH1BFJKad68eZhXcfLkyTCjdcDOnTuH26WxmMaB\nJmulteJ++eIXvxhmtE4DjXsp8doytF36HB3jlFIaHx/HvIru7u4wo2cmnQv63rlzTJ+le7QVazCl\nlNLRo0fDjL47HQ8aa3NrJdEzk8YzOnf0zHq9oaGhMKP1Yeg9bv/+/WFGa3+lxON7V1dXmHV2duJ2\nq3rPe94TZnQv05qMr776apjljgeN4bTuz9jYGG63ipUrV4YZvZfkxlL6brQWH10bufuszjpM/mVH\nkiRJUpGc7EiSJEkqkpMdSZIkSUVysiNJkiSpSE52JEmSJBXJyY4kSZKkIjWunqYaPqqdy9VwUvUw\n1Tv29fWF2d69e3Gfr732GuZVrF69OsyoCjNX70c1nvRZOlZU4Z37bFXvfve7w4yqfHNVy3WPJVXp\n5qqFm9SDT6F7gn5z7nhQTjWr9Jtyvzd3zVbx3ve+N8zoe+dqnimve6xyctXDVQwMDIQZ1UtTrW0O\n/WaqAM0dK6p2reozn/lMmDUZn+j6oFrTtra2MFu8eDHusxXjR93rms4FPYtz55iuydzyEq1AdcpU\njV/3ms+NefRMpeNBSynMpHqalt44ceJEmFFlMtUw5+5B+u70rkTZTOzYsSPM6B2CxtN169aFGb1/\npsTjO9WG0+eqorGLjkXuPqaacPosjYe5sXRkZATzW/EvO5IkSZKK5GRHkiRJUpGc7EiSJEkqkpMd\nSZIkSUVysiNJkiSpSE52JEmSJBVp1k3qNJYkSZKk31D+ZUeSJElSkZzsSJIkSSqSkx1JkiRJRXKy\nI0mSJKlITnYkSZIkFcnJjiRJkqQiOdmRJEmSVCQnO5IkSZKK5GRHkiRJUpGc7EiSJEkq0pymG3j4\n4YfDbPfu3WH20EMP4XY3bNgQZitXrgyzJUuW1MpSSmnevHlh1tbWhp+dMnv27DC7ceNGmM2fPx+3\nu2zZsjDr7u4Os40bN9bKUkpp3bp1YfYXf/EX+Nkp3/rWt8KMjunSpUtxu/TZRYsW1dru4sWLcZ+z\nZs3CvIpVq1aFGV0fCxcuxO3+9m//dpg9+eSTYfbggw+GGV1XKfHxyF3PU65du1Zr+zdv3qy93atX\nr4bZ5cuXw2xychL3OTo6GmZ33XUXfnYK3S+9vb1h9uKLL+J2n3vuuTB79dVXw2z16tVh9sd//Me4\nz7e97W21sumGhobCjMbzuXPn4nZpnCZ03dE1lxJfdzRmTdfX1xdmNH5cuHAhzM6ePRtmR48exe9z\n4MCBMDt48GCY9ff3h9mePXtwn9PVHZPpedrR0RFma9aswe3SM7OzszPM6Fr+9Kc/jfucbuvWrWFG\n49P169fDjK7N3HMp984VoXtl3759lbczMjJSax/0HLhy5UqY0T2YEh9nQt+16rNl06ZNYfbWt741\nzN785jfjdnt6esLs9ttvr/W53PsDXcvt7e23/Hf/siNJkiSpSE52JEmSJBXJyY4kSZKkIjnZkSRJ\nklQkJzuSJEmSitS4jY2aXKhx5fz587jdrq6uMKNmCmrLyTVl3HZb87nfAw88EGbUMLF8+XLcLh0P\nakxav359mFEzWErcSlPVU089FWbUmJRrRluxYkWY0bGiJr/c8aAmt3e84x342SnUxkItL7lrk5qg\n6LqjbM4cHh7qNlpV3UeTe5nQZymjcSellCYmJmp/pyl0zx07dizM6NpJKT/eRrZt2xZmNNallNId\nd9xRa5/TjY2NhRldHwsWLMDt0thD9xq1fzW5Jqu6ePFimFGDFB1Hujao/S2llA4dOhRmL7zwQpid\nOnUKt1vVJz/5yTCj8YnOP41JueuKmstorG1F02duOzRG0PVBTX5NjgedA7qWZ4Ja9wjdy/Tdcs8I\nek7T+WnF8aAmwajBLKV8My69q9G9dOnSpTAbHBzEfdK8I2qB9i87kiRJkorkZEeSJElSkZzsSJIk\nSSqSkx1JkiRJRXKyI0mSJKlITnYkSZIkFcnJjiRJkqQiNV5nZ2RkJMzOnDkTZrke7Z6enjCjznFa\nx6RVXfbk0UcfDTPqfM+taUNrx3R2doYZrSuT2yd15Ff185//PMxozYhcX/28efPCjNbgoTWJqIc+\npZTWrl0bZlXX2aFrl3r26femxGsW0PoOtN3c2j5v9P1E66jQfZ4Sr2dA19bk5GSYjY+P4z6p+7+q\nuuu/0O9NiY8XXfePPPJImO3YsQP32d3djXkVtB4LrU2WW6dr4cKFYVb3HDRRdayltSloPRS6dnNr\nNBFaR4OOFd3bM/HBD36w1udo/zRG0/iQ+yyduybnYLo777wzzGgtFVoTkdbgyX1vej+ktWxadX3Q\ne2bd5xc9P3LHo+5aOnTtbNiwAfc55bHHHguznTt3htndd9+N26V3TBofent7w+zkyZO4z71794aZ\n6+xIkiRJ+j/FyY4kSZKkIjnZkSRJklQkJzuSJEmSiuRkR5IkSVKRnOxIkiRJKlLj6mmq0hsaGgqz\nXE0r1WjSPqmycPbs2bhPqsmriir8qOqZaqlT4ppV2i7VTS5duhT3mTteVVBtIdXlUtViSlz/SN97\nwYIFYZarM25FPSj9Ltp/7tqk6lqq4aWa3V9HVTv9ZqombVLxSZXnNO7QeJZSSn19fZhXQdW29Jty\n1dN0r1OFNI1nufGjFdfPgQMHwqxJrX5bW1uY0b30RlW10/g+HY17dC/RfU7Pni1btuD3WbJkSZjR\nOThy5Ahut6qtW7eGGY0fNA5TTTfVMKfEVcu0z9z9W9UTTzwRZlQvfezYsTA7evRomOXe44aHh8OM\njiWdu5n42te+FmZ0v9Z99uSWzKh7/9L3eeCBB3CfU+65554w27hxY5jRkhsp8femCulf/vKXYfbS\nSy/hPp9++ukw+8u//Mtb/rt/2ZEkSZJUJCc7kiRJkorkZEeSJElSkZzsSJIkSSqSkx1JkiRJRXKy\nI0mSJKlIjbuWqUqPalxzlYWjo6O19kly1aBUz1kVVXFS3SnVA6eU0sKFC8Ns/vz5YdakTrsV1bEf\n+chHwoxqXHPVk1TVSZ+layD3e1txfVBNI1VmUx1uSm/M9ZE7HlTzXvXaofNIxyo3BlC9NFXLnjt3\nLkC2mjIAAByeSURBVMyoRjMlrkmu6vz582FGx4rGnZRS2rRpU5jt3r07zNatW4fbJZcvXw4zuian\ne/bZZ8OMKlFXr16N2+3s7AwzqoGm45wbH1pR5U/3FY0R9N1oDMidf6pTptry3L1UVd2lBOouX5Eb\nd+h4UK08vSvNxFve8pYwoxro1157Lcyomry3txe/D113TY5zVZ/97GfDbGJiIszoXNG4lnvu0fsH\n3b+0hMinPvUp3OcUqoKn94fctUnV5M8880yYff/73w+zl19+GfdJz+mIf9mRJEmSVCQnO5IkSZKK\n5GRHkiRJUpGc7EiSJEkqkpMdSZIkSUVysiNJkiSpSI2rp6nekepfh4aGcLtUwUrVf/R9ctXTragG\npUpLqqXMVbHSduk402/O1TvTZ5cuXYqfnbJx48Ywo7rtJjXQdB5pu02qlquiY0r1j1Q9mRIfSzpW\n9Jtzv7cV1dN172W6l1Lie4Jq7QcGBsLs0KFDuM+9e/diXgVVrlI1KdWKpsSVyVu2bAkzqmHOoYrv\nqtXTe/bsCTOql85VJvf09IQZVVqvXLkyzHJjYtXfTKgmmqr8aWyha4O2mRKPAWvWrAkzOsYzQXXs\nNEZQRpXEdE2nxGNL3Wwmtm3bFmYjIyNhVndZhSbPCHr/GBsbq/V9Xo/eI6lSma4rknuHzN1PkVYs\nA0LX9eHDh8MsVz39yiuvhNmPfvSjMKNnLdWdp5TS+vXrMb8V/7IjSZIkqUhOdiRJkiQVycmOJEmS\npCI52ZEkSZJUJCc7kiRJkorkZEeSJElSkZzsSJIkSSpS43V2qEed1snI9cpTJzx13dM6Fbnu9Fas\nG0Jd5nW77FPi70a/i9Yxqdv5nlL1dXaoL5/WDsqhNUcoo9+c68hvxTpMtA1aC+ONWsOD7hda0yMl\nvr8XLFhQaf90fdJ1TfdZSnzdDQ8Ph1lfX1+Y7d+/H/d54MABzKug9ZIo6+rqwu3S+k60lk6TtWEm\nJydrf3bKwYMHw+zs2bNhRuc4JX6+0LoStH5PZ2cn7pPOX1V0Huuus0P3Ko2lue9D104r1g1Jid8F\naGyhc0xjR5P3lrrvNDNBayYRWj+qbpYSH69W/WbyJ3/yJ2FGz2K6rul+yY2XdN3TukN11/2Z7sSJ\nE2FG6xHR51LiMZrG4d27d4fZHXfcgfvcvHkz5rfiX3YkSZIkFcnJjiRJkqQiOdmRJEmSVCQnO5Ik\nSZKK5GRHkiRJUpGc7EiSJEkqUuPqaaqfpfq+XO0w1TRSnSFV0l68eBH3SdWdVSsdBwYGwuzSpUth\nlqthpN/c1tYWZvSbcjWJVHe9detW/OwUqvKl75arPaZK1Lo1q7kqbsq3b9+On51Cv4u+d5NzRfcE\nZXRvp8TVrt3d3fjZKVRPTLXYVB2bEldpDg4OhhnVbB4/fhz3eebMGcyroPNI1fVUH50SV57WrUOl\nLCW+Pqqi5wRVBOfuZTqWdI/SMy13vyxbtgzzKnL7iNA5bkWl/ky1qnqaqm3p+qNr59y5c2FG40pK\n/JyuW3c9E7QPGk/puqr7zEqpfpV+q2qpP/zhD9faP40PlOXuJRozm7wfVnH48OEwGxoaCrPcc43e\nIXbt2hVmjz/+eJjddddduE9aAiDiX3YkSZIkFcnJjiRJkqQiOdmRJEmSVCQnO5IkSZKK5GRHkiRJ\nUpGc7EiSJEkq0qyb1HUqSZIkSb+h/MuOJEmSpCI52ZEkSZJUJCc7kiRJkorkZEeSJElSkZzsSJIk\nSSqSkx1JkiRJRXKyI0mSJKlITnYkSZIkFcnJjiRJkqQiOdmRJEmSVCQnO5IkSZKKNOeN3Pg3vvGN\nMPuXf/kX/OwLL7wQZtevXw+zxYsXh9mCBQtwn+Sll16q9N99/OMfD7Of/vSnYXbkyBHc7vz588Ns\nx44dYXb33XeHWXd3N+5z4cKFYfaJT3wCPztl1qxZYTZ37twwW7ZsGW63ra0tzJYvXx5m7e3ttffZ\n0dERZp///Ofxs1P+4R/+IcyWLl0aZosWLcLt0vGoe6zo/KeU0pw58fCRu7aqmJycDLMLFy7gZ8+d\nOxdmQ0NDYXbq1KkwO3r0KO7zxIkTYfbUU0/hZ6dcvHgxzOheoiznxo0bLc9SSunatWthRvfSdPv2\n7au1fxpbUuJrm+41ynLPF7pfqpo9e3aY0fGg58fGjRvD7JFHHsHv88QTT4TZrl27wmzdunVhlhuH\np6Pxa2xsLMzqXjs0RqfE1/WSJUvCjH7HD3/4Q9zndJcuXQoz+l10XTVBx5ne42jsp+fZTNBz4NCh\nQ2FGY9L+/ftxn/SMGBkZCTN6r/3mN7+J+5yydevWMFuxYkWY5a75efPmhdnNmzfD7PLly2E2MDCA\n+3z11VdnvE//siNJkiSpSE52JEmSJBXJyY4kSZK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aa/UkSnPtq1evqrVW/56h0pw89l0g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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110a2f510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P5wsAEvPyeDwsD4jIdnKys5WskA+9Akm3h/Ynu9PSSWcDoW+prsNkDS4zxZR3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\nOzCQhobjkkzGVzoM3ag/gPji0e9Al0QioXRHIesnoXorvZuc8yge+RP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1yy23qKysTNXV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\nABcw2AIAAAAAF/z/8e6VeXUyo9MAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110a2f9d0>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km_1000 = MiniBatchKMeans(n_clusters=1000, batch_size=1000, verbose=0, n_init=3, random_state=42)\n",
      "%time km_1000.fit(patches_normalized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 24.8 s, sys: 1.13 s, total: 25.9 s\n",
        "Wall time: 23.6 s\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
        "        init_size=None, max_iter=100, max_no_improvement=10,\n",
        "        n_clusters=1000, n_init=3, random_state=42,\n",
        "        reassignment_ratio=0.01, tol=0.0, verbose=0)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centroids_1000 = normalize(km_1000.cluster_centers_)\n",
      "activations_1000 = np.dot(patches_normalized, centroids_1000.T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(centroids_1000)\n",
      "plot_activations(activations_1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Zz7zmf/cvO5IkSZKq5GRHkiRJUpWc7EiSJEmqkpMdSZIkSVVysiNJkiSpSh23sS1YsCDN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\nhY4oV1M3QfugOmV6ziJ4/KB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VriN9HroG3aqebrvMAI0PbWv8I/h9S7+JS+/iJqj6fOHC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\nTpqxko575JFH4numXPilRucO9OaonD17tlnb3Nxs1t58881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       "text": [
        "<matplotlib.figure.Figure at 0x11c7e9ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ompby8vsHWp01IqVY14mIiCgTrkZIymmaBkCvWmm2eTCn3z8QLS0nUtJ9vgFo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\n3gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c7e9710>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mquantiles(activations_1000, prob=0.9, axis=0).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "0.53739747665851156"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.image import extract_patches_2d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time patches_0 = extract_patches_2d(faces_train[0], (8, 8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 1.17 ms, sys: 782 \u00b5s, total: 1.96 ms\n",
        "Wall time: 1.4 ms\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patches_0.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "(3249, 8, 8)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(patches_0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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unFnL0nNsX2o7X7+qHnrvxJ7TLPeuqqkYVt+x5vG8KKpn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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11098ba50>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import linalg\n",
      "from sklearn.utils import array2d, as_float_array\n",
      "from sklearn.base import TransformerMixin, BaseEstimator\n",
      " \n",
      " \n",
      "class ZCA(BaseEstimator, TransformerMixin):\n",
      " \n",
      "    def __init__(self, regularization=10**-5, copy=False):\n",
      "        self.regularization = regularization\n",
      "        self.copy = copy\n",
      "     \n",
      "    def fit(self, X, y=None):\n",
      "        X = array2d(X)\n",
      "        X = as_float_array(X, copy = self.copy)\n",
      "        self.mean_ = np.mean(X, axis=0)\n",
      "        X -= self.mean_\n",
      "        sigma = np.dot(X.T, X) # / X.shape[1]\n",
      "        U, S, V = linalg.svd(sigma)\n",
      "        tmp = np.dot(U, np.diag(1 / np.sqrt(S + self.regularization))) * np.sqrt(X.shape[0])\n",
      "        self.components_ = np.dot(tmp, U.T)\n",
      "        return self\n",
      "     \n",
      "    def transform(self, X):\n",
      "        X = array2d(X)\n",
      "        X_transformed = X - self.mean_\n",
      "        X_transformed = np.dot(X_transformed, self.components_.T)\n",
      "        return X_transformed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time patches_zca = ZCA().fit_transform(patches)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 279 ms, sys: 55.6 ms, total: 335 ms\n",
        "Wall time: 279 ms\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patches_zca.std(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "array([ 0.99999995,  0.99999991,  0.99999991,  0.9999999 ,  0.9999999 ,\n",
        "        0.99999991,  0.99999991,  0.99999995,  0.9999999 ,  0.99999986,\n",
        "        0.99999986,  0.99999985,  0.99999985,  0.99999985,  0.99999985,\n",
        "        0.99999989,  0.99999989,  0.99999985,  0.99999984,  0.99999984,\n",
        "        0.99999984,  0.99999984,  0.99999984,  0.99999988,  0.99999988,\n",
        "        0.99999985,  0.99999984,  0.99999984,  0.99999984,  0.99999984,\n",
        "        0.99999984,  0.99999988,  0.99999988,  0.99999985,  0.99999984,\n",
        "        0.99999984,  0.99999984,  0.99999984,  0.99999984,  0.99999988,\n",
        "        0.99999989,  0.99999985,  0.99999984,  0.99999985,  0.99999985,\n",
        "        0.99999984,  0.99999984,  0.99999988,  0.9999999 ,  0.99999987,\n",
        "        0.99999986,  0.99999986,  0.99999986,  0.99999986,  0.99999986,\n",
        "        0.9999999 ,  0.99999995,  0.99999992,  0.99999991,  0.99999991,\n",
        "        0.99999991,  0.99999991,  0.99999991,  0.99999995])"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patches_zca_normalized = normalize(patches_zca)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(patches_zca_normalized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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X4xqCr2J2FdGuAjZnzpx2zotRLTxt2jSZuffbXWch+HPL1VLnyZNHZtWrV7dz\n/vjjjzaPY8SIETJzdbl33XWXHffkyZMyu+eee2Q2a9YsmT344IN2TlezPmjQIPvaVK4+eNy4cTJr\n1qyZHbdp06Yy+/TTTyOP61yKFy9u86hnXhxFixaVmasWLl++vMy6d+9+Qa8LwT9T27dvLzN3Lp+P\nm266SWbufu4qops3by6ztWvX2uNZuHChzMqVKyezi3EvDcHf692zx71X+/btk1nU5+jqjlevXi2z\nqPMurtatW8vslVdekdnQoUNl5r5fzJ8/3x6P2yJiwYIFMmvQoIEdN44ZM2bI7JlnnpGZq8sOwded\nu+vsk08+uaAshBDmzZtn83Phlx0AAAAAicRiBwAAAEAisdgBAAAAkEgsdgAAAAAkEosdAAAAAInE\nYgcAAABAIqWcOXPmzP/2QQAAAADAxcYvOwAAAAASicUOAAAAgERisQMAAAAgkVjsAAAAAEgkFjsA\nAAAAEonFDgAAAIBEYrEDAAAAIJFY7AAAAABIJBY7AAAAABKJxQ4AAACARGKxAwAAACCR0qd1gCee\neEJm+/btk1lKSoodt2fPnjLbvHmzzF555RWZVatWzc5ZpEgRmT366KP2tak+++wzmc2ZM0dm77zz\njh33H//4h8xOnz4tsxdffFFm3377rZ2zadOmMqtZs6Z9baqbb75ZZuXKlZNZzpw57bhHjhyRWe7c\nuWVWoUIFmdWuXdvOWbp0aZmdPHnSvjbVbbfdJrMCBQrILEeOHHbcTZs2yaxo0aIy27lzp8wyZsxo\n5zx16pTMPvjgA/vaVAMHDpTZoUOHZFa+fHk7rvu7ChcuLLPDhw/LzF1nIYRw1VVXySxv3rz2tanc\nPeLnn3+W2ahRo+y4jRo1klnLli1l5j4Dd16FEMLSpUtlduedd9rXpnr44Ydl5q7X66+/3o77+eef\ny+zAgQMyc39z5syZ7Zzufle/fn372lRff/11rP/vr1q1aiWzNWvWyMw9T0Pw94gePXrIzD3/69Wr\nZ+c82w033CCzjh07ymzx4sUyq1ixosyOHj1qj8d9xm+88YbM3Ofz5JNP2jnP1r9/f5m57z/79++X\nWcmSJWWWLl06ezyrVq2SmfsM3P373XfftXOerWDBgjJ79tlnZXb77bfLLGvWrDIbMmSIPZ5PP/1U\nZpUqVZKZe/a4Mc/mzqPWrVvLLOqc37Jli8xuvfVWmbn3yn3fDSGEdu3ayWz48OHn/O/8sgMAAAAg\nkVjsAAA9oWoqAAAgAElEQVQAAEgkFjsAAAAAEonFDgAAAIBEYrEDAAAAIJHS3MZWpUoVmeXJk0dm\nrjErhBBWrFghM9f+0Lx5c5mlT+//3Kh2oTimTJkisxo1ashs5MiRdlzXbPfJJ5/IzDUETZgwwc7p\nGs/itrG5RpwlS5bIbP78+Xbcrl27yuy3336T2cqVK2VWrFgxO+fF4Nq2GjRoILNdu3bZcV072YIF\nC2SWIUMGmbnWmRDiN9A57vycPXu2zKLOj1KlSsnszJkzMnP3JXdehRDCtm3bZOZaxc62Z88emV1x\nxRUyc00+IfgGI/c+d+nSRWYHDx60c7r2wrjWrVsns3nz5snsnnvuseN27txZZpMmTZKZa4l0jVoh\nhDBu3DiZxW1jc/dM9+x74IEHZPbYY4/J7PXXX7fH465D1zCaKVMmmbnn2V+5lkp3bytTpozMXPuU\ne/9D8I1n7l7XsGFDO25cbpzs2bPLzLV9vfbaazIbMWKEPR7XAum+X0Q9e+Jy8+fPn19mo0ePltll\nl10ms2zZstnjuf/++2U2dOhQmblrOy7XQrp8+XKZRX0vzpUrl8zc93jXDufa8EKIf788G7/sAAAA\nAEgkFjsAAAAAEonFDgAAAIBEYrEDAAAAIJFY7AAAAABIJBY7AAAAABKJxQ4AAACARErzPjtubxDX\nK1+2bFk7rtvrYPv27TJzXfJRe8OcOHHC5nG4/Rs++OADmbl+/hBCmD59uszcvgBunxu3D1IIIcyY\nMcPmcVx99dUyu++++2Q2fPhwO+6wYcNk9swzz8js999/l5nbjyaE6A79ONq2bSszt9+J298jhBD+\n9a9/yez48eMyc/sZvPTSS3bOjBkz2jyOjRs3ymz16tUyu/vuu+24ixYtktnll18us6jzznF70sS1\ndu1ambl9wgoUKGDHdfvsvPXWWzL7z3/+I7NevXrZOTdt2mTzOP7nf/5HZseOHZNZ1Od44403yszd\ns9w+XVH7Crl9duJy+8O0bNlSZkOGDJGZOzei9g5yuXuPp02bZseNK1++fDJz55/bg8ddZ+77Tggh\nDBw4UGZ169aVmXu+n88eK24/vieeeEJmbs+9ypUry+z666+3x+POO7eXjduv8Hy4Z9TevXtlVqJE\nCZm5Pe/q1Kljj2fixIkX9Nr169fbceNIly6dzNx3wcWLF9txn3rqKZlVqFBBZs2aNZNZkyZN7Jzu\n3Onevfs5/zu/7AAAAABIJBY7AAAAABKJxQ4AAACARGKxAwAAACCRWOwAAAAASCQWOwAAAAASKc3V\n04cPH5aZq8v98ccf7biuBvibb76RWaVKlWQWVYW6ZcsWmblaz7NdccUVMpszZ47Mnn/+eTtu165d\nZeZqq139p6siDCGEgwcP2jwOV3fpqmujqmP/+c9/yqx9+/Yye+CBB2R2+vRpO2f+/PltHkeHDh1k\n5ipPd+/ebcd1n3P9+vVlVqtWLZkNHTrUztmuXTubx+EqVx9++GGZDRgwwI7rzg933G+88YbMXFV6\nCL562lVlxuXqcqPO3T///FNm7u96+eWXZfb666/bOXv37m3zOO69916ZHThwQGZu24MQQpg5c6bM\nChcuLLNChQrJzFXShuArz+Navny5zNw9c9u2bTJzldmuwj2EEP744w+Zuffjkkv0v7O6Z+hfLVu2\nTGauVr9YsWIyc5XE7pwLIYQzZ87IrHz58jLLlCmTHTeut99+W2Zuaw23dYH77uOuoxBCeOWVV2RW\no0YNmV2MZ20IIeTNm1dmrrre3S/d9yL3DA8hhCuvvFJmrpY6d+7cdtw43LYs7jp67LHH7Liuetq9\nx65SfdWqVXZOd+4o/LIDAAAAIJFY7AAAAABIJBY7AAAAABKJxQ4AAACARGKxAwAAACCRWOwAAAAA\nSKQ0V09/+OGHMnM1nadOnbLjuopHV4Xn6ibLlStn5xwyZIjN45g9e7bMnn32WZk99NBDdlxXh+kq\nevPkySMzVxsawsWpns6RI4fMnnnmGZm5ysoQQli6dKnMXAXr119/LTNX2xlCCNWrV7d5HCNGjJBZ\n2bJlZRZVPemqS4sXLy6znTt3ysy9xyGE8OSTT8rM1YqfLSUlRWZz586VWYsWLey43bt3l9natWtl\ndsMNN8hs/Pjxds4vvvjC5nEcOnRIZlmyZJHZyZMn7biugnX//v0yGzNmjMweffRRO6c715s2bWpf\nm2rSpEkyK1WqlMxGjRplx3VV4K4e3t1PXX1rCCGMHTvW5nG4z9E9b1esWCEzt3VBVLWwOyePHj0q\ns6gq/bjcNgOuqt09Tx1X3xtCCJUrV5bZ1q1bZRa1DURc2bJlk9ntt98uM1eZ7LbkcPfvEPx3Lncf\nvhg17SGE0KZNG5mtWbNGZoMHD5aZu++46yGEEGbMmCGzIkWKyMxdv3FNmTJFZu67z88//2zHddvI\nXHrppdEHdg5RVexR64dz4ZcdAAAAAInEYgcAAABAIrHYAQAAAJBILHYAAAAAJBKLHQAAAACJxGIH\nAAAAQCKluXraVRaWLl1aZkWLFrXjzp8/X2auwtNVWkZVf1599dU2j8NVns6ZM0dmN998sx3XvV+n\nT5+W2b59+2Tm6r1DCOHaa6+1eRzly5eX2dChQ2UWVeHoait/+eUXma1atUpmtWrVsnPWr1/f5nG4\nqs7p06fLLOp62bFjh8waNGggs8aNG8usXr16ds5bbrnF5nFcd911Mtu+fbvMXN1lCP5zvummm2TW\nsWNHmdWtW9fO+fHHH8usR48e9rWpXOXmunXrYo1xvq9192l3jUbVw7qq07hcffHIkSNl5s6rEPzf\n7OqDv/32W5lF1RJHVXXHcc0118hs/fr1MitYsOAFzVesWDGbHz58WGauSv9iVQu78+NCj23btm0y\nGzRokD2ef/7znzK7/vrrZVayZEmZPf7443bOs7Vr105m7vxwldUuq127tj2eEydOyMxdg2m5153N\nPQfcNiUuW7lypcyiKs1dvbSrf77rrrvsuHG477euFv2NN96w47rKe7cFhduKxVXKhxDCnj17bH4u\n/LIDAAAAIJFY7AAAAABIJBY7AAAAABKJxQ4AAACARGKxAwAAACCRWOwAAAAASCQWOwAAAAASKc37\n7LhOerfvRMWKFe24rq+8adOmMitQoIDM3L4MIYSwdOlSm8fh+sG//PJLmb355pt23OPHj8ts8eLF\nMnP7Cbgu+RBCyJw5s83jOHTokMxOnjwps4YNG9pxy5QpI7PChQvLrHXr1jL7+9//bufMlSuXzePI\nkCGDzEqVKiWzzZs323Fz5Mghs6lTp8qsWrVqMsuYMaOdMyqPo1u3bjJz+/w8+OCDdtw+ffrIzL3P\n7lrq16+fnfOtt96yeRxVqlSRmdvTbP/+/XZcdw78+eefMqtatarMovZuadasmc3j2LBhg8zcdf7E\nE0/YcZs0aSIzd89yn8+yZcvsnG4PpbiWLFkiM7dn1n//+1+ZNW/eXGZt2rSxxzNixAiZLV++XGbu\nWfzQQw/ZOc/WoUMHmU2bNk1mK1askJnbm2748OH2eNz3oXz58snsYuxpF4L//uPOa7en2ZYtW2S2\nadMmezy7du2Smbse0qVLJzO3T9pfub2W3Ptx++23y8zt8Tdu3Dh7PPPmzZOZ2yuvbdu2dtw43HXe\nu3dvmR05csSO6/b+Gj16tMzcvobu8w/BPxcUftkBAAAAkEgsdgAAAAAkEosdAAAAAInEYgcAAABA\nIrHYAQAAAJBILHYAAAAAJFKaq6dvvfVWmbl6OFfRGEIITz/9tMwmTJggs9OnT8ts/Pjxdk5Xk1iz\nZk372lTub+7Ro4fMxo4da8ctXry4zO666y6ZudpIV9sZgq9YjKtnz54ymzNnjsxWrlxpxx06dKjM\nXA1zx44dZfbLL7/YOStUqCCzFi1a2NemcufnqVOnZOaqtkMIYevWrTLLnj27zP74448Lel0Ivgo5\nLncOuvr5lJQUO66rYq5Ro4bMXD1sVCWuq3COa/fu3TJz70dUBeyrr74qs/Lly8vMnQPuvQohhNq1\na9s8Dne9uBr3qNrrhQsXyixLliwyy507t8yiqqVd1X5crgr8q6++uqDXrV69WmbZsmWzx1O9enWZ\n1a9fX2au2vd8zJ07V2b58+eXmasddlsM9OrVyx6P+wzce+WeZ+cjZ86cMqtUqZLM3GflKtXdMyuE\nEIoUKSIz9/y4GDXtIfhtBtx3LnevP3HihMyintMfffSRzFyV/6BBg+y4cfTt21dmH3zwgczeeecd\nO+6qVatkFvWMUA4cOGDzqPvSufDLDgAAAIBEYrEDAAAAIJFY7AAAAABIJBY7AAAAABKJxQ4AAACA\nRGKxAwAAACCRUs6cOXPmf/sgAAAAAOBi45cdAAAAAInEYgcAAABAIrHYAQAAAJBILHYAAAAAJBKL\nHQAAAACJxGIHAAAAQCKx2AEAAACQSCx2AAAAACQSix0AAAAAicRiBwAAAEAisdgBAAAAkEjp0zrA\nc889J7Pt27fL7Pjx43bckydPyiwlJUVmefLkkVmOHDnsnCdOnJDZq6++al+bqnjx4jIrV66czAoX\nLmzHPXTokMwyZswos9y5c8vszJkzds6FCxfKbM6cOfa1qfr16ycz916tW7fOjuv+5mLFisnskkv0\n+n7//v12zuzZs8vs+uuvt69NtWDBApk9++yzMjt8+LAdN1++fDKrW7euzNq3by+zP/74w855ww03\nyCzq3Erl3o+1a9fK7MiRI3Zcd39x58DmzZtltmXLFjtnzZo1ZXbvvffa16b66quvZOY+j40bN9px\nJ06cKLPhw4fL7J///KfMpk6dauf89ddfZdagQQP72lTHjh2T2c033yyz77//3o47btw4mc2cOVNm\n7hninlkhhPDEE0/YPI4qVarIbNCgQTLbu3evzNy9Y8qUKfZ4Bg4cKLOOHTvKrG3btjJr3bq1nfNs\nXbt2lVmlSpVkli1bNpm5e4f7fhGC/3wyZ84ss9WrV8vsxhtvtHOerW/fvjJz9+TRo0fL7PPPP5fZ\nb7/9Zo/HfVcbP368zNznWqdOHTvn2b788kuZZc2aVWbufupe574jRL3WfefZtWuXzF544QU7Z6rO\nnTvL7Ntvv5WZe86HEMKPP/4os4ceekhmpUuXllnU8/3yyy+XWdOmTc/53/llBwAAAEAisdgBAAAA\nkEgsdgAAAAAkEosdAAAAAInEYgcAAABAIqW5jc01WLn2kYIFC9pxs2TJIrMMGTLIzDUmrV+/3s4Z\n1aYTx5133ikz1z4R1fTkWj6WLFkiszVr1sgsb968ds5SpUrZPI5FixbJzDXQueMOIYRGjRrJbM+e\nPTJz50DVqlXtnAcOHLB5HN98843M3n33XZktX77cjutazaZNmyYz16TirrMQ4jeuOa4Fxp0f6dP7\nW5c7Ntdsc+rUKZm5pqWoOeNy19wXX3whs2bNmtlxr7rqKplt3bpVZi1btpRZzpw57Zw9evSQWVST\nW6q33npLZt26dZOZazYMIYSyZcvK7OjRozJz50fU8+Ptt9+W2aOPPmpfm8p9zq7Nyd27XGtq1DNz\nzJgxMmvcuLHM4rZXRnFNk67xb+zYsTIrU6aMzP788894B3YO7l7nmhbPp41t6dKlMnOte+5ads+W\n119/3R7PddddJ7NevXrJzN0fzqeNzbXRuu8YrjXNPRddy18IIezYsUNm7jtg1LMnDteM9swzz8jM\nvYchhPDII4/IbOXKlTJzDbPuu3IIvimYNjYAAAAA/7/CYgcAAABAIrHYAQAAAJBILHYAAAAAJBKL\nHQAAAACJxGIHAAAAQCKluXo6Y8aMMjtx4oTMdu7cacd1VZlz586VWa1atWRWsWJFO+eRI0dsHoer\nkHS1kK5KOQRfEXzo0CGZNWzYUGZXXnmlndPVJMZVv359ma1du1Zm7rwKwddGlixZUmZFihSRWdTf\nu3fvXpvH4c7rfv36ycxVAIfgq3TLlSsnM1cBP2XKFDtnSkqKzOLWMH/++ecy69u3r8xmzJhhxy1W\nrJjMateuLTNXaekqmkMIoUSJEjaPw1X9bt68WWZR91NXAbpt2zaZvfzyyzJbtmyZndNtSxDXqlWr\nZPbVV1/JLKpGfuDAgTLr2rWrzAoVKiSzqDrtqDr9OHLlyiUzVx/saqmLFy8us6iK99mzZ8vsrrvu\nktmFVMeei3umz5o1S2bu/jBz5kyZucrqEELo2bOnzPLnzy+zVq1a2XHjct9hKleuLDN3vbRp00Zm\n7v4dQgiZMmWS2auvviqzzp0723Hj2rRpk8zc1ijuuJ2o161evVpm7ly+GPdSN8aKFStk1rFjRzuu\nu+/t27dPZq42Purvjdr24Fz4ZQcAAABAIrHYAQAAAJBILHYAAAAAJBKLHQAAAACJxGIHAAAAQCKx\n2AEAAACQSGmunt6wYYMe3NRWusrbEHwd6pNPPimzcePGycwdawgXp97vzTfflNm3334rs+nTp9tx\nS5cuLTNXO3zs2DGZubrBEEKYP3++zB588EH72lRZs2aV2fHjx2XmKl5DCGHjxo0ycxXA7px0tb8h\nhJAtWzabx3HnnXfK7JVXXpFZVAWsez9cZfFll10ms6hqclcdHpebY8GCBTI7deqUHde9X9u3b5eZ\nq4d152sI/lqLy1V1tm3bVmbjx4+34/bq1Utmrq62Zs2aMouqF4+6huOoXr26zFyNe9S9/oYbbpCZ\n+wxeeOEFmT366KN2zhtvvNHmcSxZskRmLVq0kJmr8q9Ro4bMJk2aZI9n165dMnNVx+75fj7cPcI9\ne9w90X0PiKpbv//++2X23HPPycw938/HqFGjZPbUU0/J7KWXXpKZu9e67QdC8NsHuPrv3Llz23Hj\nypAhwwVl7m+uUKGCzKLq592zx91ro97nON5//32Zub+pcePGdtxFixbJzG3nUbRoUZm5rRJCCKFA\ngQI2Pxd+2QEAAACQSCx2AAAAACQSix0AAAAAicRiBwAAAEAisdgBAAAAkEgsdgAAAAAkEosdAAAA\nAImU5n12cubMKbPs2bPLbO7cuXbcWrVqycx1mbs9EqL2SXH7DcTl9ry57rrrZOb20AghhM8//1xm\nq1atklnmzJlltnjxYjtno0aNbB7H4cOHZVayZEmZRe15tGfPHpm5/XIyZcokM9e7H0J093scH3zw\ngcxuvfVWmY0ZM8aOe+mll8rM7aOyevVqmU2YMMHO6Y437r4RlSpVkpk7d3fv3m3HdXsA1alTR2Zu\nfwV3PwshhNOnT9s8jrx588ps69atMkvLHlClSpWS2YcffiizqM+4X79+F3pI/5/ChQtfUBa155Hb\nt8LtG3H99dfLLOoePm3aNJvHUbt2bZm5e+bJkydl5vbKGTx4sD2eNm3ayMxdZ6+//rodNy53P1+z\nZo3MypcvL7MqVarIbMiQIfZ43F42bn/AJk2ayKxbt252zrM9//zzMuvZs6fM3LV0yy23yOz333+3\nxzNy5EiZXX755TI7ceKEHTeuqL3AFHd/cN8/O3fubMd178evv/4qs4uxD9OcOXNk9tprr8nMfWcJ\nIYQ8efLIzO215r57uPMxhAvb449fdgAAAAAkEosdAAAAAInEYgcAAABAIrHYAQAAAJBILHYAAAAA\nJBKLHQAAAACJlObq6dy5c8usYMGCMnvnnXfsuJMnT5bZY489JjNX3RpVR+oqi+NauHChzFxd3vHj\nx+24rtbU1W9u27ZNZgcPHrRz7t271+ZxuFpC91lt377djlukSBGZuSrVdevWyczVZIfgq3/jKl68\nuMxcbXq1atXsuO6zcvXOBw4ckFn+/PntnKNHj5ZZixYt7GtT7dixQ2a5cuWSmaumD8HX8LpK6507\nd8rMfXYh+OrQuNz16qrRXeV9CCH06tVLZq5qecGCBTJ77rnn7JwTJ06U2c0332xfm8qdY1dccYXM\nqlevbsd1tae9e/eWmavh7d+/v53zb3/7m83jyJEjxwW9zj2n3bVSt25dO27GjBll5u79rgr7fLhr\nuUCBAjJz15J7fkRVIi9ZskRmr7zyisy++eYbO25c8+bNk1n37t1l1qFDB5n16dNHZlu2bLHH457j\ny5cvl1mZMmXsuHG589Od902bNpVZx44dZRb13GvdurXMpk+fLrOo70NxNGvWTGZFixaVWdT3ZvfM\nctuLuNdFPd/ddyWFX3YAAAAAJBKLHQAAAACJxGIHAAAAQCKx2AEAAACQSCx2AAAAACQSix0AAAAA\niZTm6ulx48bJrG/fvjKLqkr96quvZObqDl3dZJQSJUpc8GtTXXbZZTK7+uqrZRZV7zdr1iyZuZo+\nV7Fav359O+f3338vs4cffti+NpWrfjx9+rTMoj5HV126atUqmbm6w9q1a9s5XX1rXK5S031WUcfm\nKoLd+3H33XfLbPjw4XZOV8Mb19KlS2Xm6sXz5ctnx23UqJHM3P3DnZO7d++2c7qazbjcOeZquqPO\nD1fh3KVLF5kNGjRIZu+9956ds3379jaPw9Wmjxw5UmZHjx6147p7sasQv/baa2XmapBD8M+0m266\nyb42VbFixWR2odeSqx5320eEEEL69PorxIoVK2QWVaUf16FDh2TmtmSoWLGizCZMmCAzt11CCH5r\nA3dvuRjfPUIIoUGDBjJzz4jvvvtOZjNnzpTZyy+/bI/HVcw3b95cZm6bDvfZ/ZU7B9xz0VVEu+ee\nu1+GEMLAgQNl5q5f93fEdeutt8rMVZa7CvkQQmjcuLHMXI37iy++KLPHH3/czhn1LD4XftkBAAAA\nkEgsdgAAAAAkEosdAAAAAInEYgcAAABAIrHYAQAAAJBILHYAAAAAJFKaq6cfe+wxmfXq1UtmhQsX\ntuO6ClZXd+nqP0+cOGHndFWZcT399NMye+GFF2RWoUIFO27v3r1lVrduXZn98ssvMhs2bJid8803\n37R5HJUrV5bZ/v37Zda0aVM7rqs8f/DBB2V2xx13yKxmzZp2zsWLF9s8jqJFi8rMVXxGVem2bNlS\nZtOmTZPZwoULZVa8eHE7586dO20eh6tMfu2112QWVYnbvXt3mbnjdvcAV6MeQghTp061eRyugv6K\nK66Q2WeffWbHHTFihMxatGghsz59+sjsYtUHO+6+6Gp2oz4rV/Puznt3vh45csTO6Z6HceXIkUNm\n7pm6d+9embnq2Kj6+QEDBshszJgxMnO1v+fDPdOzZMkiM1fz664zd48OwV8TrpbabaVwPsaPHy8z\n9zyfPXu2zFy9t6utD8HXFk+cOFFmderUsePGtWnTJplVrVpVZuXLl5fZxo0bZTZ48GB7PB9++KHM\nUlJSZFaoUCE7bhxu242xY8fKzB1zCP7+sWbNGpm5WurOnTvbOW+77TaZqW0n+GUHAAAAQCKx2AEA\nAACQSCx2AAAAACQSix0AAAAAicRiBwAAAEAisdgBAAAAkEgpZ86cOfO/fRAAAAAAcLHxyw4AAACA\nRGKxAwAAACCRWOwAAAAASCQWOwAAAAASicUOAAAAgERisQMAAAAgkVjsAAAAAEgkFjsAAAAAEonF\nDgAAAIBEYrEDAAAAIJFY7AAAAABIpPT/NwefMGGCzC65xK+zNm/eLLPs2bPL7OjRoxc8Z5YsWWTW\nvn17+9pUr7/+usyOHz8us8OHD9txCxYsKLMDBw7I7MiRIzJLly6dnTNHjhwye/zxx+1rUz300EMy\n69Spk8xmzJhhx23atKnMpk2bJrOyZcvKbOfOnXZOd97deeed9rWp+vXrJ7ODBw/KrHTp0nbcQ4cO\nyeznn3+W2ZNPPimzhQsX2jkXLVoks9dee82+NtWIESNktmXLFplFfVbuvXTXWv78+WWWM2dOO+es\nWbNk9uWXX9rXpkpJSZHZiy++KLOaNWvacd374ezevVtmUffTP//8U2a9e/eONf911113QfO///77\ndty1a9fKbMeOHTLbv3+/zPLmzWvn7NChg8zOnDljX5vq6quvlln//v1lVq5cOZn16dNHZs8//7w9\nntatW8ssX758MnPP9ylTptg5zzZu3DiZuedi1qxZZXbq1CmZuWdiCCEULlxYZu6+446nUqVKds6z\nPffcczJzn/OCBQtktmLFCpnNmzfPHo/7m924S5culdm6devsnGebOXOmzJYtWyazWrVqyey3336T\nWZMmTezxbNq0SWbu+8X27dtldsMNN9g5U3Xp0kVmLVu2lJm7VkPwz+lt27bJbPny5TJz38VDCGH2\n7Nk2Pxd+2QEAAACQSCx2AAAAACQSix0AAAAAicRiBwAAAEAisdgBAAAAkEhpbmPbsGGDzDJnziwz\n10oRQgjly5eXmWvRcK1DVapUsXMeO3bM5nG49rOTJ0/KLFeuXHZc1zLnjts1FmXKlMnOGXVMcbgm\nmcWLF8usVatWdtyvvvpKZkWKFJHZ/PnzZVavXj075+rVq20eh2tVcS1AefLkseO6BrpbbrlFZh9/\n/LHMLr/8cjtnw4YNbR6H+5tdA6G7t0SN61rTfv31V5m5prYQQmjUqJHN43ANRUuWLJFZ1Pmxb98+\nmbn7wKWXXiqzIUOG2Dnvuecem8fx9ttvy8w1NrmmxxBC6NWrl8xcu5FrcYsS1VwVR9u2bWX24Ycf\nyszdh2vUqCEz14YXgm8Rc9doVKtZXO5+etVVV8ls7ty5Mjtx4oTMoq4z1xSWIUMGmV2MZ20I/n3d\ntWuXzD777DOZ3XfffTJz9+gQfDuda4PNmDGjHTcu15zmrmX3PHWtuuPHj7fHkz69/srt2hwrV65s\nx43DfVbuuB944AE7rjvnfv/9d5lt3LhRZq7JM4QQPv/8c5mp1jl+2QEAAACQSCx2AAAAACQSix0A\nAAAAicRiBwAAAEAisdgBAAAAkEgsdgAAAAAkEosdAAAAAImU5n12XI+26/aP8u2338rs7rvvltnk\nyZNl5vbgCSGEvXv3Rh5XFNdl7vbgOXz4sB3X9c7nzJnzgsZ1ne8hRHedx+He89y5c8ts0qRJdtzq\n1QfxCp0AACAASURBVKvLzO3h1KJFC5l9+umnds46derYPI6qVavKzB131B4/zZo1k5nbC6N06dIy\nmzNnjp2zUKFCNo/DdfRPnz5dZsWLF7fjuh7+atWqyczthVGzZk07pzuf41qzZo3M/vjjD5l17drV\njjto0CCZDR48WGY9e/aU2YMPPmjnnDBhgszc/j1nc/sDFShQQGb333+/Hfenn366oOy2226TWdQ5\neTGul//85z8ye/bZZ2XmntMtW7aUWePGje3xuGfE0KFDZda+fXs7blzuelm5cuUFvc49M901GILf\ny8btz+L2BCpbtqyd82wpKSkyc3uIuefiqFGjZBZ1bG4fJLd3YJs2bey4cbn3w+1r07p1a5m5Z7Hb\nsyoE/4zfs2ePzNx5FZfbp6158+YyGzt2rB33zJkzMnP3D7eXort3hOD3WFP4ZQcAAABAIrHYAQAA\nAJBILHYAAAAAJBKLHQAAAACJxGIHAAAAQCKx2AEAAACQSGmunnY1fMuWLZNZVE3rtddeKzNXZ5wu\nXTqZuYrmEELInz+/zeNwFYE7d+684LldHeb+/ftl5upOs2XLZufctm2bzeO40LrNF154wY7rzg9X\nh7lhwwaZ1apVy8556tQpm8fh6ovz5csnM1dbHkIIW7duldnp06dllitXLpm1bdvWzumutbh++OEH\nmbl7xEcffWTHLVmypMxcJfJ9990nM1dVGkIIXbp0sXkc06ZNk1nTpk1lNnPmTDtugwYNZDZ69GiZ\nuXMgqg49c+bMNo/D3YNc1bOrUg0hhJMnT8ps8+bNMps4caLM3HscQghbtmyRWVRVdipXEeyet64C\nduPGjTLr1KmTPZ7Zs2fLzN2Thg8fLrO+ffvaOc/m/q4ZM2bIzN0fXCWye4aH4L9j5MmTR2au0vp8\narovpJI3BF/1nClTJpmtX7/ejuveyypVqsjMXYOucv6vrr76apmdOHFCZm4LE1cvHbV9hfvOU6ZM\nGZkVKVLEjhuH24LCfYd013gIITRs2FBm7h7gPkf3uYXg/xaFX3YAAAAAJBKLHQAAAACJxGIHAAAA\nQCKx2AEAAACQSCx2AAAAACQSix0AAAAAiZTm6uknnnhCZvfcc4/MXI1qCL6m01XSZs+eXWarVq2y\nc1arVs3mcaRPr99SV/N75swZO66rvMyQIYPMXPWzq6UOIYTChQvbPI7KlSvLbMCAATJzFY0hhLBn\nzx6ZuVrCxYsXy6x69ep2zjp16tg8Dldb6eoW3etCCCFLliwyO3DgQPSBncN///tfm7vz+eGHH441\nhzvHnn/+eZlFVVOuWbNGZu7vSklJuaAshBCeeuopmf3rX/+yr01VoUIFmbnq871799pxixUrJjNX\nLTt27FiZtWrVys5ZqVIlm8fhKvld1XLUvS2qZl655BL974Mff/yxfa2ryo5r+/btMnPPEHctffDB\nBzKLqp11zztX/+1qmM/HokWLZNarVy+Z3XHHHTJz9zV3HYXg69Z37959QXOeD1df7GqgR40aJbNm\nzZrJLOoaP3TokMzcPcttl3A+XnvtNZm56umiRYvKzG0Dctttt9njcd9B3TYM5cuXl9mNN95o50y1\nbt26CxrfbZcRQgg7duyQWdWqVWV2+eWXy+z222+3c7rnpMIvOwAAAAASicUOAAAAgERisQMAAAAg\nkVjsAAAAAEgkFjsAAAAAEonFDgAAAIBESnP1tKt3dBV948aNs+O2bt1aZnPnzpVZhw4dZLZx40Y7\n59atW20ex08//SQzV6OaI0cOO66r/ytbtqzMXGXx999/b+f87rvvZNa9e3f72lSuGrRgwYIyK126\ntB3XnT/uvNu1a5fMXBVlCCFs3rxZZnFrqYcMGSKzf//73zL7+uuv7biuArZdu3Yyc7WRruY2BF8f\nG7d6ev78+TK76aabZBZ1vbhq4U2bNslsw4YNMouqQ3X133G56nx3f3L10SH4OnZXD+uqdt2xhhDC\nwoULZdaiRQv72lQ5c+aM9f/91fr1623uzt1s2bLJzL2Pb7zxhp3zYjxf3HnfqFEjmQ0aNEhmH330\nkcxKlChhj8fd98aMGSOzqOr4uI4dOyYzV8W9fPlymbn66P3799vjceer+26QN29eO25czz33nMym\nTp0qM3cPcM/TqMpsV03tni+uJvl8VKxYUWZ//PGHzFxNfKdOnWTmtmIJIYQmTZrIzFVPf/jhh3bc\nONyz1l3ntWvXtuO67U3cM8BdZ66WOoQL2waEX3YAAAAAJBKLHQAAAACJxGIHAAAAQCKx2AEAAACQ\nSCx2AAAAACQSix0AAAAAicRiBwAAAEAipXmfHddjfvToUZlF7eHh9sJwHdu//PKLzDJmzGjndHso\nxDVs2DCZXXKJXltG7Sfh3o8333xTZh07dpRZ8+bN7ZwX0mX+V26/JHcOuG7/EPx+B6tWrZKZ2xtk\nwIABdk7XU+/2dDhbly5dZHb77bfLbOXKlXbcTz75RGYDBw6UWcmSJWXm9tAKIYQpU6bYPI61a9fK\nzHX/u310QvD7zrj7krtHuNeFEEKRIkVsHkepUqVkNnbsWJm5/SxC8O+H2z/I/c0HDx60c7r9F+Jy\n+8q4fcui7h/p0qWTmXsOuPNjxYoVds59+/bZPI4DBw7IbNu2bTJ74YUXZOb22bnlllvs8bjzw+37\nM2LECJn9/e9/t3OerVu3bjL7+eefZeb2j3J7w7nXheCfPddcc43MLnQ/qb9yn/OpU6dk5r5ftG3b\nVma///67PR6335V7Frtr+3z07dtXZvXr15dZoUKFZOa+t9StW9cezwMPPCCzt956S2YXY5+dhx56\nSGY//vijzMqUKWPHdc+WAgUKyMzdy6KeLdOmTZOZeo/5ZQcAAABAIrHYAQAAAJBILHYAAAAAJBKL\nHQAAAACJxGIHAAAAQCKx2AEAAACQSGmunh41apTMdu/eLTNXeRuCr0OtUaOGzHLnzi2zqGpYV4UY\n1x133CEzV0sZVWvraqtbtGghM1cPHFXv56pu42rSpInM+vXrJ7Oo2lpXTVm2bFmZufro5cuX2zkr\nV65s8zjatGkjs2uvvVZmUefmXXfdJTNX5bpw4UKZpaSk2Dlz5cpl8zjcde6qQUuXLm3H3blzp8zc\nObBnzx6ZubriEPw1GtfNN98sM1crG1VrvGHDBpm5z9HVME+dOtXOGXX+xLFgwQKZubrevHnz2nG3\nbt0qs2zZsl3QnFmyZLFzXoz3w81/9913y8xV17otCC699FJ7PDt27JDZyJEjZeYqxc/H8OHDZebq\nx3ft2iWzdevWycz9TSGEcOTIEZm5qt3NmzfLbObMmXbOs7kK+unTp8usZcuWMnN/U9WqVe3xuC0Z\nMmTIILOLca2EEMKQIUNk5iqVixYtKjNX8X7o0CF7PO56ceNGbT0RR9OmTWX266+/yuzjjz+245Yr\nV05m7juT+z5esGBBO6dbWyj8sgMAAAAgkVjsAAAAAEgkFjsAAAAAEonFDgAAAIBEYrEDAAAAIJFY\n7AAAAABIpJQzrhsQAAAAAP4fxS87AAAAABKJxQ4AAACARGKxAwAAACCRWOwAAAAASCQWOwAAAAAS\nicUOAAAAgERisQMAAAAgkVjsAAAAAEgkFjsAAAAAEonFDgAAAIBESp/WAQYPHiyzRYsWyaxZs2Z2\n3BUrVshsw4YNMuvUqZPMjh07Zufcvn27zG677Tb72lTXXHONzKpVqyazXbt22XH37t0rs4wZM8ps\n//79Mjt16pSd071fkydPtq9N9e6778qsQ4cOMps+fbodN3fu3DLLkiWLzAoWLCizSZMm2Tmvu+46\nmZUpU8a+NtWAAQNk9v3338ssQ4YMdtycOXPKLG/evDJz51WBAgXsnD///LPM3LV/Nvc3jxo1Smbt\n27e34/bs2VNmV199tczKlSsns/Hjx9s5a9asKbNXX33VvjbVXXfdJTN3Xrv7ZQghPPLIIzIbOXKk\nzO655x6Zffrpp3bO1q1by6xjx472tal69+4ts3Tp0sns6NGjdtz8+fNfULZt2zaZFS1a1M7pnpU/\n/fSTfW2qqVOnymznzp0y27179wVl7pkYQgjZsmWTmXsuzZw5U2bDhw+3c54tJSVFZjfffLPMsmbN\nKjP3NxUvXtwez1tvvSWzSpUqyaxGjRoy69+/v53zbE899ZTMihQpIjN3vbhnT9T3Fvd3ue8mX3/9\ntcwmTJhg5zybu0fNnTtXZu5a2rJli8zcszaEEAoXLiwzdw8/n79ZyZUrl8zc87Jp06Z2XHe97Nmz\nR2Y//PCDzKKetatWrZLZmTNnzvnf+WUHAAAAQCKx2AEAAACQSCx2AAAAACQSix0AAAAAicRiBwAA\nAEAipbmNzbUHffPNNzJ78skn7bhvvPGGzOrVqyezQ4cOycy1aIQQQtmyZW0eh2vbcM0l+fLls+O6\nVhyXVa9eXWabNm2yc2bKlMnmcbg2lmHDhsmsSpUqdlzX8jFr1iyZufaeAwcO2DmffvppmX355Zf2\ntameeOIJmVWsWFFmUeeHa1Rav369zNxnvHbtWjuna62K69FHH5WZa64bNGiQHbdVq1YyK1SokMyW\nLVsmszZt2tg5161bZ/M43Gfl3quDBw/acfv06SMzd426ayKqqat06dI2j+OSS/S/x7kWoKhz0x17\n+vT6seiyqPtH1apVbR6HayFy90TX5OfeR9daFoJv+HLnco4cOey4canmpRBCKFWqlMxca9bp06dl\nVrt2bXs8Q4YMkdlNN90ks6j2wLjcNefOT3fuuObTqHPaNW7VqlVLZhMnTrTjxrV161aZ7dix44Ky\nG2+8UWbuO00IvpHXfTdx34ddk9rZXMPZiBEjZPbwww/bcVu0aCEz9/3CXUtR59V//vMfm58Lv+wA\nAAAASCQWOwAAAAASicUOAAAAgERisQMAAAAgkVjsAAAAAEgkFjsAAAAAEonFDgAAAIBESvM+O82b\nN5dZjx49ZFa8eHE7bs6cOWVWokQJmU2ePFlmGTNmtHO6fQH+T3v3GWxVmaZ9/EZykpwFQbJgIEgY\nEDAAImFabMCAoA5qo4I93dLBbhUHG7UcFZiWNgyDGNGx7VYQBTE0IAdEyTknSSI5HILwfjpVVBfX\ntRbhwzur/r+P/ms/a3PO3mvvx1N1P2m5Gf1uXr2bBx8RUaNGDdnc2Q/Lly+XrWrVqvaabvZ+Wm7u\nv3vef/nLX+y6Xbt2la1ixYqybdq0SbZatWrZa77wwguypT1n5/bbb5dt0aJFsq1Zs8auu3PnTtnc\nmRaTJ0+W7f7777fX/PLLL21Po0+fPrJNnz5dNneeVYQ/X+qdd96R7YEHHpDNneEV4X9/abnXrjt3\n6NFHH7XrtmnTRjb3s3Lv36SfR69evWRbtWqVfWye48ePy3b48OGzahH+vV6mTJnkJ3YaBQsWtP18\nnKXifh5ne792Z+l8//339rHuM839e5Pev2kNGDBANvfcmjVrJtvevXtlc2e4RfjXjjvPqkKFCnbd\ntNznRP78+WVzZ4+58wmTXtM33nijbPPnz5ftfJxJFeHPtVmwYIFs7dq1k+3DDz+UzX03jfBn6fz0\n009nvW4aH3/8sWzuHvD666/bdd3ZQu714c6lc98NIyLmzZtn++nwlx0AAAAAmcRmBwAAAEAmsdkB\nAAAAkElsdgAAAABkEpsdAAAAAJnEZgcAAABAJp3z6OkHH3xQthUrVsiWNHoyJydHNjfeuWjRorK5\nsZ0RfvxeWu4aO3bskM39myL8aOHt27fLVrduXdmSxkYmjf9L44IL9H56//79sv385z+363777bey\nFS5cWDY3crZevXr2mqNGjbI9jXLlyslWokQJ2UaPHm3Xfe6552RbsmSJbFOmTJEt6f0ydOjQs2qn\nuuSSS2RzY9MPHTpk1502bZps3bp1k23ixImyuRHNERH9+/e3PQ03ktmt/8wzz9h13Wt30KBBsrlx\nua5FRPTu3dv2NCZNmiSbGx+c9Pni3hNuZLEbxezG1Uacn1H+7p7sjnNwn8XucyBp5O0PP/wgm3t9\n3HDDDXbdtNzv0R2t4MZLnzx5UjZ3j46IWLZsmWxuzP75GsXt7lELFy6Uzb0G3PeipCNE3Pc4126+\n+Wa7blozZsyQzX0X2Ldvn2zude2+p0VErF27VrYrr7xSNvdZmJZ73u676bPPPmvXde+z5s2by1aq\nVCnZ3O8tIuKPf/yjbE8++eRp/zt/2QEAAACQSWx2AAAAAGQSmx0AAAAAmcRmBwAAAEAmsdkBAAAA\nkElsdgAAAABk0jnPFnbj4zp16iTbV199Zdd1I6RnzZolmxvf58bsRkSUL1/e9jTcKE43hrlKlSp2\n3V27dslWrVo12Y4dOyabG7EZEbFp0ybb0/j8889lK1mypGwLFiyw67qflxvv7EbHbtu2zV7TjfFO\ny42mdK+dd999167btGlT2a655hrZ3EjapPGPAwcOtD2NzZs3y7Z48WLZatSoYdd1r4/Dhw/L5kZ8\nutdVRMT69etl69u3r31smjXcKHA3RjUiYvr06bJ17NhRttmzZ8t29dVX22veddddtqcxZ84c2dzr\nL+le/tZbb8nmRtC7144aeZpG2lHtZcqUkW3+/PmyuXu9e11dccUV9vm4z4iVK1fKNm7cONmSvhuc\nqmbNmrK551akSBHZGjVqJJv7HI7wn7fud9y1a1fZevXqZa95Kvf54l7XGzdulO3gwYOyHTlyxD4f\nN7p8woQJsp2PYy8i/BEVbmx5+/btZVu3bp1sSSPV3eeL+25Sp04du24aTz31lGz9+vWT7eGHH7br\nuu8Q7giIFi1ayOa+/0dEtG7d2vbT4S87AAAAADKJzQ4AAACATGKzAwAAACCT2OwAAAAAyCQ2OwAA\nAAAyic0OAAAAgEw65/l+1113nWxutGC7du3sum5ksRuvuGrVKtny5ctnr5k0WjaNhg0byubGVo4d\nO9au60aHurGBX375pWxJo2OTehodOnSQ7c9//rNsO3bssOsOHz5cthEjRsjmXjv169e31+zcubPt\nabiRmj//+c9lSxo93aRJE9ncOOPc3FzZkkZL/8d//Idst912m31sHjf2Nmm8tONGom7fvl0297w/\n/PBDe01373nppZfsY/P07t1bNjfWdvXq1Xbdq666SjY36nfPnj2yJY1id6PUK1SoYB+b58CBA7K5\nMczueUf4MbzFixeXzY2nTxov3rJlS9vTcO+Xo0ePyrZ3717Z3Hhpd72IiLlz58rWuHFj2dwI3jPh\nXgOVK1c+qzXdERFly5a1j3X3gDfeeEO2IUOGJD+xFE6cOCG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WL18umxt3HuHH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110a74690>"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(patches)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s2WefTbMdO3bg8YwePTrN6P6le/Bi0Dso/Vb0nkCtm9SoGhExY8aMNKPvXBpr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\nU2lGxzp+/HjcblVU50z3BL1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k1ZKTHUmSJEm11OM+u2XLlqXZjBkz0owqXiO4Iphq56je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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12f21a490>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "km_1000_zca = MiniBatchKMeans(n_clusters=1000, batch_size=1000, verbose=0, n_init=3, random_state=42)\n",
      "%time km_1000_zca.fit(patches_zca_normalized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 34 s, sys: 1.84 s, total: 35.8 s\n",
        "Wall time: 34.5 s\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
        "        init_size=None, max_iter=100, max_no_improvement=10,\n",
        "        n_clusters=1000, n_init=3, random_state=42,\n",
        "        reassignment_ratio=0.01, tol=0.0, verbose=0)"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centroids_1000_zca = normalize(km_1000_zca.cluster_centers_)\n",
      "activations_1000_zca = np.dot(patches_zca_normalized, centroids_1000_zca.T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_patches(centroids_1000_zca)\n",
      "plot_activations(activations_1000_zca)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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M1eGGqq7VXKuu165du8pxk/owro9Urw2z1K+P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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11a1a1810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nCADQb51f7HbP1eN8vs49WwAA9A33NQJAK/WL3Y2S3UZmamSLqWEAAAAA+iVT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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11a1a1fd0>"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q90 = mquantiles(activations_1000_zca, prob=0.9, axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q90.min(), np.median(q90), q90.mean(), q90.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "(0.098011454849816881,\n",
        " 0.16891624485345302,\n",
        " 0.16801724987873315,\n",
        " 0.33636003134520914)"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.plot(sorted(np.sum(activations_1000_zca > q90.mean(), axis=0)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "[<matplotlib.lines.Line2D at 0x11b4679d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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t+paSAjNnwuzZoY5MZPDQmIUMWB0d8I//aG1Qc+ut1ro+V14Z6qhEpCdKFtIn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\n0iMlCxER6ZGShYiI9EjJQkREevT/ASS1haFpqa03AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12e0ab410>"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}